Complex systems, such as vehicles, aircraft, spacecraft and other systems, typically include many subsystems for controlling and managing the vehicle. Throughout the specification, it should be understood that a reference to a vehicle encompasses a variety of complex systems. It is desirable to identify adverse events or failures that may be occurring in one or more of these subsystems, particularly during real-time operation of the vehicle. Integrated Vehicle Health Management systems may be used to monitor and diagnose various characteristics (failure modes) of the vehicle. Model-based systems using models, rules, decisions, and cases (i.e., expert systems) about system behavior have been developed to create a functional model of a system that receives and evaluates inputs from sensors and other data sources within the vehicle. However, prior art models are integrated only at a subsystem level instead of encompassing an entire vehicle. In addition, a large amount of time and resources are required to develop and update the model in response to hardware and software upgrades over the life-cycle of the vehicle.
A rules based approach to engineering diagnostics solutions may use built-in test (BIT) data. BIT Engineering (considered to be a rules based approach) is not very effective for complex systems and is a main reason for high false alarm (FA) rates. The engineering effort involved in producing good rules is very costly and unaffordable in software and hardware upgrades over decades; therefore, the costs of maintaining these systems is immense—nearly 72% of the life-cycle cost goes in operations and support. In addition, equipment BIT information may be supplied by different vendors and generally does not follow a consistent standard.